Summary of Beyond Autoregression: Discrete Diffusion For Complex Reasoning and Planning, by Jiacheng Ye et al.
Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning
by Jiacheng Ye, Jiahui Gao, Shansan Gong, Lin Zheng, Xin Jiang, Zhenguo Li, Lingpeng Kong
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Autoregressive language models have limitations in complex reasoning and long-term planning tasks. We propose discrete diffusion models as a novel solution to overcome these challenges. Our approach, Multi-Granularity Diffusion Modeling (MGDM), prioritizes subgoals based on difficulty during learning. MGDM outperforms autoregressive models without using search techniques on complex tasks like Countdown, Sudoku, and Boolean Satisfiability Problems. For instance, MGDM achieves 91.5% accuracy on Countdown and 100% accuracy on Sudoku, whereas autoregressive models achieve 45.8% and 20.7%, respectively. Our work demonstrates the potential of diffusion-based approaches in advancing AI capabilities for sophisticated language understanding and problem-solving tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models have a hard time solving complex puzzles and planning ahead. We came up with a new way to solve this problem using something called discrete diffusion models. These models are good at learning difficult steps that other models can’t handle. We created a special approach called Multi-Granularity Diffusion Modeling (MGDM) that helps the model focus on the most important steps first. MGDM is better than other models at solving puzzles like Countdown, Sudoku, and logical problems. For example, MGDM gets 91.5% of answers correct on Countdown and 100% correct on Sudoku, while other models get 45.8% and 20.7%, respectively. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Language understanding
